RAG (Retrieval-Augmented Generation) focuses on enhancing AI responses by retrieving external data, while MCP (Model Context Protocol) standardizes how AI interacts with various data sources and tools. Overview of RAG Definition: RAG is an AI architecture that improves the accuracy and relevance of responses generated by large language models (LLMs) by pulling in up-to-date information from external sources, such as databases or APIs, before generating a reply. 2 Functionality: When a user submits a query, RAG retrieves relevant content from connected data sources and appends this information to the input prompt, enriching the model's context with real-world relevance. This helps reduce inaccuracies and hallucinations in AI responses by grounding them in verifiable sources. 2 Use Cases: RAG is particularly useful in scenarios where real-time data is crucial, such as customer support, news aggregation, and any application requiring current information. 3 Sources Overview of MCP Defi...
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